1
|
Zhang X, Zhao S, Su X, Xu L. From docking to dynamics: Unveiling the potential non-peptide and non-covalent inhibitors of M pro from natural products. Comput Biol Med 2024; 181:108963. [PMID: 39216402 DOI: 10.1016/j.compbiomed.2024.108963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/05/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024]
Abstract
MOTIVATION This study aims to investigate non-covalent and non-peptide inhibitors of Mpro, a crucial protein target, by employing a comprehensive approach that integrates molecular docking, molecular dynamics simulations, and top-hits activity predictions. The focus is on elucidating the non-covalent and non-peptide binding modes of potential inhibitors with Mpro. METHODS We employed a semi-flexible molecular docking methodology, binding score and ADME screening, which are based on structure, to screen compounds from CMNPD and HERB in silico. These methodologies allowed us to find potential candidates depending on their binding values and interactions with the binding site of main protease. To further evaluate the stability of these interactions, we conducted molecular dynamics simulations and calculated binding energies. Ultimately, a top-hits activity prediction method was employed to prioritize compounds based on their predicted inhibitory potential. RESULTS Through a combination of binding energy calculations and activity predictions, we identified six potential inhibitor molecules exhibiting promising activity against Mpro. These compounds demonstrated favorable binding interactions and stability profiles, making them attractive candidates for further experimental validation and drug development efforts targeting Mpro.
Collapse
Affiliation(s)
- Xin Zhang
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Shulin Zhao
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Xi Su
- Foshan Women and Children Hospital, Foshan, China
| | - Lifeng Xu
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
| |
Collapse
|
2
|
Zhao H, Li H, Liu Q, Dong G, Hou C, Li Y, Zhao Y. Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications. Methods 2024; 221:82-90. [PMID: 38104883 DOI: 10.1016/j.ymeth.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/14/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
MOTIVATION The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative. RESULT In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.
Collapse
Affiliation(s)
- Hongxi Zhao
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China
| | - Hongfei Li
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China; College of Life Science, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China
| | - Qiaoming Liu
- School of Medicine and Health, Harbin Institute of Technology, 150001, Harbin, HeiLongJiang, China; Zhengzhou Research Institute, Harbin Institute of Technology, 450000, Zhengzhou, Henan, China
| | - Guanghui Dong
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China
| | - Chang Hou
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China
| | - Yang Li
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China.
| | - Yuming Zhao
- College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China; College of Life Science, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China.
| |
Collapse
|
3
|
Wang S, Li J, Wang D, Xu D, Jin J, Wang Y. Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection Representation. IEEE J Biomed Health Inform 2023; 27:5165-5176. [PMID: 37527303 DOI: 10.1109/jbhi.2023.3300717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.
Collapse
|
4
|
Zhu W, Yuan SS, Li J, Huang CB, Lin H, Liao B. A First Computational Frame for Recognizing Heparin-Binding Protein. Diagnostics (Basel) 2023; 13:2465. [PMID: 37510209 PMCID: PMC10377868 DOI: 10.3390/diagnostics13142465] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
Collapse
Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Shi-Shi Yuan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| |
Collapse
|
5
|
Sun Y, Zhao B, Wang Y, Chen Z, Zhang H, Qu L, Zhao Y, Song J. Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy. Phys Chem Chem Phys 2022; 24:29940-29951. [PMID: 36468652 DOI: 10.1039/d2cp03681a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.
Collapse
Affiliation(s)
- Yujia Sun
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Bodi Zhao
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Yuqi Wang
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Zitong Chen
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Huaiyu Zhang
- Institute of Computational Quantum Chemistry, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang, Hebei, 050024, P. R. China
| | - Lingbo Qu
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Yuan Zhao
- The Key Laboratory of Natural Medicine and Immuno - Engineering, Henan University, Kaifeng, Henan, 475000, P. R. China
| | - Jinshuai Song
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| |
Collapse
|
6
|
Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
|
7
|
iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network. Methods 2022; 208:1-8. [DOI: 10.1016/j.ymeth.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 11/07/2022] Open
|
8
|
Xiao J, Liu M, Huang Q, Sun Z, Ning L, Duan J, Zhu S, Huang J, Lin H, Yang H. Analysis and modeling of myopia-related factors based on questionnaire survey. Comput Biol Med 2022; 150:106162. [PMID: 36252365 DOI: 10.1016/j.compbiomed.2022.106162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/12/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
With the rapid development of science and technology, the trend of low age myopia is becoming increasingly significant. The latest national survey done by the Chinese government found that more than 80% of Chinese teenagers suffer from myopia. Adolescent myopia is closely related to living environment, heredity, and living habits. Quantifying the relationship between myopia and living environment, heredity, and living habits is conductive to the prevention and intervention of adolescent myopia. In this study, we investigated the relationships between four main factors (environment, habits, parental vision, and demographic) and myopia status by analyzing the questionnaire data. Data were collected from Chengdu, China in 2021, including 2808 myopia samples and 5693 non-myopia samples, with a total of 22 features. Then, these 22 features were inputted into three machine learning algorithms to discriminate the two classes of samples. Results show that the computational model could produce an AUC of 0.768. To pick out the most important features which play important roles in classification, we used incremental feature selection strategy to screen the 22 features. As a result, we found that the 4 most influential features with XGBoost could achieve a competitive AUC of 0.764. To further investigate the risk and protective factors affecting adolescent myopia, we used OR values derived from MLE-LR to analyze the relationship between 22 features and adolescent myopia. Results showed that the age variable was the most significant risk factor for myopia, followed by the myopia status of parents. The most protective factor for eyesight is the measure taken by the children, followed by the distance between books and eyes when reading. These discoveries can guide the prevention and control of myopia in children and adolescents.
Collapse
Affiliation(s)
- Jianqiang Xiao
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Mujiexin Liu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Qinlai Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zijie Sun
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Junguo Duan
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Siquan Zhu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Jian Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Hui Yang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Computer Science, Chengdu University of Information Technology, Chengdu, 611844, China.
| |
Collapse
|
9
|
Ren L, Xu Y, Ning L, Pan X, Li Y, Zhao Q, Pang B, Huang J, Deng K, Zhang Y. TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms. IMETA 2022; 1:e42. [PMID: 36245702 PMCID: PMC9537919 DOI: 10.1002/imt2.42] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/06/2022] [Accepted: 07/10/2022] [Indexed: 12/15/2022]
Abstract
In China, traditional Chinese medicine (TCM) has been widely used for coronavirus infectious disease 2019 (COVID-19) prevention, treatment, and recovery and has played a part in the battle against the disease. A variety of TCM treatments have been recommended for different stages of COVID-19. But, to the best of our knowledge, a comprehensive database for storing and organizing anti-COVID TCM treatments is still lacking. Herein, we developed TCM2COVID, a manually curated resource of anti-COVID TCM formulas, natural products (NPs), and herbs. The current version of TCM2COVID (1) documents over 280 TCM formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information; (2) records over 80 NPs with detailed potential therapeutic mechanisms; and (3) launches a useful web server for querying, analyzing and visualizing documented formulas similar to those supplied by the user (formula similarity analysis). In summary, TCM2COVD provides a user-friendly and practical platform for documenting, querying, and browsing anti-COVID TCM treatments, and will help in the development and elucidation of the mechanisms of action of new anti-COVID TCM therapies to support the fight against the COVID-19 epidemic. TCM2COVID is freely available at http://zhangy-lab.cn/tcm2covid/.
Collapse
Affiliation(s)
- Liping Ren
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for InterdisciplineChengdu University of Traditional Chinese MedicineChengduChina
- School of Healthcare TechnologyChengdu Neusoft UniversityChengduChina
| | - Yi Xu
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of China (UESTC)ChengduChina
| | - Lin Ning
- School of Healthcare TechnologyChengdu Neusoft UniversityChengduChina
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of China (UESTC)ChengduChina
| | - Xianrun Pan
- College of Medical TechnologyChengdu University of Traditional Chinese MedicineChengduChina
| | - Yuchen Li
- School of Healthcare TechnologyChengdu Neusoft UniversityChengduChina
| | - Qi Zhao
- College of Food and Biological EngineeringChengdu UniversityChengduChina
| | - Bo Pang
- Beijing CapitalBio Technology Co., Ltd.BeijingChina
| | - Jian Huang
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of China (UESTC)ChengduChina
| | - Kejun Deng
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of China (UESTC)ChengduChina
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for InterdisciplineChengdu University of Traditional Chinese MedicineChengduChina
| |
Collapse
|
10
|
Yuan SS, Gao D, Xie XQ, Ma CY, Su W, Zhang ZY, Zheng Y, Ding H. IBPred: a sequence-based predictor for identifying ion binding protein in phage. Comput Struct Biotechnol J 2022; 20:4942-4951. [PMID: 36147670 PMCID: PMC9474292 DOI: 10.1016/j.csbj.2022.08.053] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
Ion binding proteins (IBPs) can selectively and non-covalently interact with ions. IBPs in phages also play an important role in biological processes. Therefore, accurate identification of IBPs is necessary for understanding their biological functions and molecular mechanisms that involve binding to ions. Since molecular biology experimental methods are still labor-intensive and cost-ineffective in identifying IBPs, it is helpful to develop computational methods to identify IBPs quickly and efficiently. In this work, a random forest (RF)-based model was constructed to quickly identify IBPs. Based on the protein sequence information and residues’ physicochemical properties, the dipeptide composition combined with the physicochemical correlation between two residues were proposed for the extraction of features. A feature selection technique called analysis of variance (ANOVA) was used to exclude redundant information. By comparing with other classified methods, we demonstrated that our method could identify IBPs accurately. Based on the model, a Python package named IBPred was built with the source code which can be accessed at https://github.com/ShishiYuan/IBPred.
Collapse
|
11
|
Elend L, Jacobsen L, Cofala T, Prellberg J, Teusch T, Kramer O, Solov’yov IA. Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations. Molecules 2022; 27:molecules27134020. [PMID: 35807268 PMCID: PMC9268208 DOI: 10.3390/molecules27134020] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 12/10/2022] Open
Abstract
Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.
Collapse
Affiliation(s)
- Lars Elend
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Luise Jacobsen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark;
| | - Tim Cofala
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Jonas Prellberg
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Thomas Teusch
- Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany;
| | - Oliver Kramer
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
- Correspondence: (O.K.); (I.A.S.); Tel.: +49-441-798-3817 (I.A.S.)
| | - Ilia A. Solov’yov
- Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany;
- Research Center for Neurosensory Science, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
- Center for Nanoscale Dynamics (CENAD), Carl von Ossietzky Universität Oldenburg, Institut für Physik, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
- Correspondence: (O.K.); (I.A.S.); Tel.: +49-441-798-3817 (I.A.S.)
| |
Collapse
|
12
|
Wang S, Xu D, Gao B, Yan S, Sun Y, Tang X, Jiao Y, Huang S, Zhang S. Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling. Front Oncol 2022; 12:915542. [PMID: 35747826 PMCID: PMC9209659 DOI: 10.3389/fonc.2022.915542] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Bladder cancer is a highly complex and heterogeneous malignancy. Tumor heterogeneity is a barrier to effective diagnosis and treatment of bladder cancer. Human carcinogenesis is closely related to abnormal gene expression, and DNA methylation is an important regulatory factor of gene expression. Therefore, it is of great significance for bladder cancer research to characterize tumor heterogeneity by integrating genetic and epigenetic characteristics. This study explored specific molecular subtypes based on DNA methylation status and identified subtype-specific characteristics using patient samples from the TCGA database with DNA methylation and gene expression were measured simultaneously. The results were validated using an independent cohort from GEO database. Four DNA methylation molecular subtypes of bladder cancer were obtained with different prognostic states. In addition, subtype-specific DNA methylation markers were identified using an information entropy-based algorithm to represent the unique molecular characteristics of the subtype and verified in the test set. The results of this study can provide an important reference for clinicians to make treatment decisions.
Collapse
Affiliation(s)
- Shuyu Wang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Dali Xu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shuhan Yan
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yiwei Sun
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Xinxing Tang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yanjia Jiao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Shumei Zhang, ; Shan Huang,
| | - Shumei Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Shumei Zhang, ; Shan Huang,
| |
Collapse
|
13
|
Martín V, Sanz-Novo M, León I, Redondo P, Largo A, Barrientos C. Computational study on the affinity of potential drugs to SARS-CoV-2 main protease. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:294005. [PMID: 35504274 DOI: 10.1088/1361-648x/ac6c6c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/03/2022] [Indexed: 06/14/2023]
Abstract
Herein, we report a computational investigation of the binding affinity of dexamethasone, betamethasone, chloroquine and hydroxychloroquine to SARS-CoV-2 main protease using molecular and quantum mechanics as well as molecular docking methodologies. We aim to provide information on the anti-COVID-19 mechanism of the abovementioned potential drugs against SARS-CoV-2 coronavirus. Hence, the 6w63 structure of the SARS-CoV-2 main protease was selected as potential target site for the docking analysis. The study includes an initial conformational analysis of dexamethasone, betamethasone, chloroquine and hydroxychloroquine. For the most stable conformers, a spectroscopic analysis has been carried out. In addition, global and local reactivity indexes have been calculated to predict the chemical reactivity of these molecules. The molecular docking results indicate that dexamethasone and betamethasone have a higher affinity than chloroquine and hydroxychloroquine for their theoretical 6w63 target. Additionally, dexamethasone and betamethasone show a hydrogen bond with the His41 residue of the 6w63 protein, while the interaction between chloroquine and hydroxychloroquine with this amino acid is weak. Thus, we confirm the importance of His41 amino acid as a target to inhibit the SARS-CoV-2 Mpro activity.
Collapse
Affiliation(s)
- Verónica Martín
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Miguel Sanz-Novo
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
- Grupo de Espectroscopía Molecular (GEM), Edificio Quifima, Área de Química-Física, Laboratorios de Espectroscopía y Bioespectroscopía, Parque Científico UVa, Unidad Asociada CSIC, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Iker León
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
- Grupo de Espectroscopía Molecular (GEM), Edificio Quifima, Área de Química-Física, Laboratorios de Espectroscopía y Bioespectroscopía, Parque Científico UVa, Unidad Asociada CSIC, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Pilar Redondo
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Antonio Largo
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Carmen Barrientos
- Departamento de Química Física y Química Inorgánica, Universidad de Valladolid, 47011 Valladolid, Spain
| |
Collapse
|